Ensemble models - XGboost
Without any additional information, lean towards the vote of the best performing classifier when it comes to ties. 

However, as others have stated already, it is best to analyze the performances in more detail (e.g. confusion matrices). 

For instance, it could be that model B almost always classifies class X correctly (hardly any false positives). In that case, you could lean towards the prediction of model B if it predicts class X and model A does not. In other words; you could weigh the votes of the models based on how well they did in similar, previous predictions.